Role OverviewSenior Software Engineer - Machine Learning Systems & High-Performance LLM InferenceWe are seeking a Senior Software Engineer to contribute to the development of high-performance software that powers execution of open-source large language models (LLMs) on our custom appliance. This appliance leverages a combination of FPGAs and x86 CPUs to accelerate transformer-based models. The software stack is written primarily in modern C++ (C++17/20) and heavily relies on templates, SIMD optimizations, and efficient parallel computing techniques.
Key Responsibilities- Design and implement high-performance inference software for LLMs on custom hardware.
- Develop and optimize C++-based libraries that efficiently utilize SIMD instructions, threading, and memory hierarchy.
- Work closely with FPGA and systems engineers to ensure efficient data movement and computational offloading between x86 CPUs and FPGAs.
- Optimize model execution via low-level optimizations, including vectorization, cache efficiency, and hardware-aware scheduling.
- Contribute to performance profiling tools and methodologies to analyze execution bottlenecks at the instruction and data flow levels.
- Apply NUMA-aware memory management techniques to optimize memory access patterns for large-scale inference workloads.
- Implement ML system-level optimizations such as token streaming, KV cache optimizations, and efficient batching for transformer execution.
- Collaborate with ML researchers and software engineers to integrate model quantization techniques, sparsity optimizations, and mixed-precision execution.
- Ensure all code contributions include unit, performance, acceptance, and regression tests as part of a continuous integration-based development process.
Required Qualifications- 7+ years of professional experience in C++ software development, with a focus on performance-critical applications.
- Strong understanding of C++ templates and modern memory management.
- Hands-on experience with SIMD programming (AVX-512, SSE, or equivalent) and intrinsics-based vectorization.
- Experience in high-performance computing (HPC), numerical computing, or ML inference optimization.
- Experience with ML model execution optimizations, including efficient tensor computations and memory access patterns.
- Knowledge of multi-threading, NUMA architectures, and low-level CPU optimization.
- Proficiency with systems-level software development, profiling tools (perfetto, VTune, Valgrind), and benchmarking.
- Experience working with hardware accelerators (FPGAs, GPUs, or custom ASICs) and designing efficient software-hardware interfaces.
Preferred Qualifications- Familiarity with LLVM/Clang or GCC compiler optimizations.
- Experience in LLM quantization, sparsity optimizations, and mixed-precision computation.
- Knowledge of distributed inference techniques and networking optimizations.
- Understanding of graph partitioning and execution scheduling for large-scale ML models.
Leveling & ScopeWhile this role is currently posted at a specific level, we are a growth-oriented organization and are open to hiring at a more senior level for the right candidate. Please note that this job description serves as a focused but generalized overview of the role; specific responsibilities and impact expectations will be tailored to the experience and seniority of the final hire.
Compensation and BenefitsThe base salary range for this role is $150,000 - $250,000.
Please note that the figures provided represent the
base salary range only and do not include other elements of our total compensation package, equity, or comprehensive benefits.
At Positron AI, we value the unique expertise each candidate brings. While the range above reflects our typical expectation for the position,
we reserve the flexibility to exceed this range for candidates whose specialized skills, significant experience, or unique qualifications fall outside the standard scope of the role. Final offers are determined based on a variety of factors, including internal equity, and individual impact.
